EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges
In the field of rehabilitation, the electromyography (EMG) signal plays an important role in interpreting patients’ intentions and physical conditions. Nevertheless, utilizing merely the EMG signal suffers from difficulty in recognizing slight body movements, and the detection accuracy is strongly i...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-07-01
|
| Series: | Biosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6374/10/8/85 |
| _version_ | 1827712322321252352 |
|---|---|
| author | Chaoming Fang Bowei He Yixuan Wang Jin Cao Shuo Gao |
| author_facet | Chaoming Fang Bowei He Yixuan Wang Jin Cao Shuo Gao |
| author_sort | Chaoming Fang |
| collection | DOAJ |
| description | In the field of rehabilitation, the electromyography (EMG) signal plays an important role in interpreting patients’ intentions and physical conditions. Nevertheless, utilizing merely the EMG signal suffers from difficulty in recognizing slight body movements, and the detection accuracy is strongly influenced by environmental factors. To address the above issues, multisensory integration-based EMG pattern recognition (PR) techniques have been developed in recent years, and fruitful results have been demonstrated in diverse rehabilitation scenarios, such as achieving high locomotion detection and prosthesis control accuracy. Owing to the importance and rapid development of the EMG centered multisensory fusion technologies in rehabilitation, this paper reviews both theories and applications in this emerging field. The principle of EMG signal generation and the current pattern recognition process are explained in detail, including signal preprocessing, feature extraction, classification algorithms, etc. Mechanisms of collaborations between two important multisensory fusion strategies (kinetic and kinematics) and EMG information are thoroughly explained; corresponding applications are studied, and the pros and cons are discussed. Finally, the main challenges in EMG centered multisensory pattern recognition are discussed, and a future research direction of this area is prospected. |
| first_indexed | 2024-03-10T18:12:23Z |
| format | Article |
| id | doaj.art-c406c6719598479b97f730609ae3ea85 |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-10T18:12:23Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-c406c6719598479b97f730609ae3ea852023-11-20T07:59:38ZengMDPI AGBiosensors2079-63742020-07-011088510.3390/bios10080085EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and ChallengesChaoming Fang0Bowei He1Yixuan Wang2Jin Cao3Shuo Gao4School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, ChinaDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02138, USASchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, ChinaIn the field of rehabilitation, the electromyography (EMG) signal plays an important role in interpreting patients’ intentions and physical conditions. Nevertheless, utilizing merely the EMG signal suffers from difficulty in recognizing slight body movements, and the detection accuracy is strongly influenced by environmental factors. To address the above issues, multisensory integration-based EMG pattern recognition (PR) techniques have been developed in recent years, and fruitful results have been demonstrated in diverse rehabilitation scenarios, such as achieving high locomotion detection and prosthesis control accuracy. Owing to the importance and rapid development of the EMG centered multisensory fusion technologies in rehabilitation, this paper reviews both theories and applications in this emerging field. The principle of EMG signal generation and the current pattern recognition process are explained in detail, including signal preprocessing, feature extraction, classification algorithms, etc. Mechanisms of collaborations between two important multisensory fusion strategies (kinetic and kinematics) and EMG information are thoroughly explained; corresponding applications are studied, and the pros and cons are discussed. Finally, the main challenges in EMG centered multisensory pattern recognition are discussed, and a future research direction of this area is prospected.https://www.mdpi.com/2079-6374/10/8/85multisensoryelectromyographypattern recognitionrehabilitation |
| spellingShingle | Chaoming Fang Bowei He Yixuan Wang Jin Cao Shuo Gao EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges Biosensors multisensory electromyography pattern recognition rehabilitation |
| title | EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges |
| title_full | EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges |
| title_fullStr | EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges |
| title_full_unstemmed | EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges |
| title_short | EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges |
| title_sort | emg centered multisensory based technologies for pattern recognition in rehabilitation state of the art and challenges |
| topic | multisensory electromyography pattern recognition rehabilitation |
| url | https://www.mdpi.com/2079-6374/10/8/85 |
| work_keys_str_mv | AT chaomingfang emgcenteredmultisensorybasedtechnologiesforpatternrecognitioninrehabilitationstateoftheartandchallenges AT boweihe emgcenteredmultisensorybasedtechnologiesforpatternrecognitioninrehabilitationstateoftheartandchallenges AT yixuanwang emgcenteredmultisensorybasedtechnologiesforpatternrecognitioninrehabilitationstateoftheartandchallenges AT jincao emgcenteredmultisensorybasedtechnologiesforpatternrecognitioninrehabilitationstateoftheartandchallenges AT shuogao emgcenteredmultisensorybasedtechnologiesforpatternrecognitioninrehabilitationstateoftheartandchallenges |